Living things differ from nonliving things in that they carry information stored in DNA and RNA. Much of what living cells need to do require them to integrate this stored information with information inputs from their external environments. In this way, cells are like computers, but, unlike computers, information handling by cells depends on molecules instead of electrons moving through wires. Over the past five years, we have developed and used advanced methods to study a very simple example of biological information handling, a system that a single yeast cell uses to sense and transmit information from outside to make decisions. We have learned about the molecules the cell uses to do this and some of the key operations these molecules perform. During the next five years, we will study carefully the molecular operations needed for one key portion of the information transmission system to function. We will learn how these molecular events affect the signal transmitted by the system and the information that the signal carries. In the shorter term, because closely related systems operate in all animal cells, including humans, what we learn about how the molecules operate may suggest avenues to devise drugs that manipulate their function and might afford useful therapies. Longer term, a better understanding of how molecular events perform operations on information may help guide genetic interventions, and might help contribute to development of molecular computation.

Public Health Relevance

We will study the operation of one small part of a model information sensing and transmitting system in a model cell. We will learn how specific molecular operations regulate the signal and the information the signal transmits.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM086615-02
Application #
7753902
Study Section
Molecular and Integrative Signal Transduction Study Section (MIST)
Program Officer
Maas, Stefan
Project Start
2009-01-01
Project End
2012-12-31
Budget Start
2010-01-01
Budget End
2010-12-31
Support Year
2
Fiscal Year
2010
Total Cost
$571,961
Indirect Cost
Name
Vtt/Msi Molecular Sciences Institute
Department
Type
DUNS #
941716045
City
Berkeley
State
CA
Country
United States
Zip Code
94704
Pesce, C Gustavo; Zdraljevic, Stefan; Peria, William J et al. (2018) Single-cell profiling screen identifies microtubule-dependent reduction of variability in signaling. Mol Syst Biol 14:e7390
Andrews, Steven S; Rutherford, Suzannah (2016) A Method and On-Line Tool for Maximum Likelihood Calibration of Immunoblots and Other Measurements That Are Quantified in Batches. PLoS One 11:e0149575
Andrews, Steven S; Peria, William J; Yu, Richard C et al. (2016) Push-Pull and Feedback Mechanisms Can Align Signaling System Outputs with Inputs. Cell Syst 3:444-455.e2
Robinson, Martin; Andrews, Steven S; Erban, Radek (2015) Multiscale reaction-diffusion simulations with Smoldyn. Bioinformatics 31:2406-8
Sands, Bryan; Jenkins, Patrick; Peria, William J et al. (2014) Measuring and sorting cell populations expressing isospectral fluorescent proteins with different fluorescence lifetimes. PLoS One 9:e109940
Schmidt, Hugo G; Sewitz, Sven; Andrews, Steven S et al. (2014) An integrated model of transcription factor diffusion shows the importance of intersegmental transfer and quaternary protein structure for target site finding. PLoS One 9:e108575
Claeys Bouuaert, Corentin; Lipkow, Karen; Andrews, Steven S et al. (2013) The autoregulation of a eukaryotic DNA transposon. Elife 2:e00668
Golemis, Erica A; Serebriiskii, Ilya; Finley Jr, Russell L et al. (2011) Interaction trap/two-hybrid system to identify interacting proteins. Curr Protoc Cell Biol Chapter 17:Unit 17.3.
Golemis, Erica A; Serebriiskii, Ilya; Finley Jr, Russell L et al. (2011) Interaction trap/two-hybrid system to identify interacting proteins. Curr Protoc Neurosci Chapter 4:Unit 4.4